Final Year Project by Low Jia Rong (U1730052L)

Packages and Setup

Libraries

setwd("~/Dropbox/FYP_Jia Rong/R Processing/FINAL PROCESSING/Analysis")
library(tidyverse)
library(lme4)
library(ggplot2)
library(glmmTMB)
library(correlation)
library(sjPlot)
#library(easystats)

library(report)
library(correlation)
library(parameters)
library(see)

descriptive_statistics <- function(df, begins_with){
  df %>% 
    select(dplyr::starts_with(begins_with)) %>% 
    report() %>% 
    as.data.frame() %>% 
    select(-one_of(c("n_Obs", "Median", "MAD", "n_Missing"))) %>% 
    print()
  
  plot(df %>% 
    select(dplyr::starts_with(begins_with)) %>% 
    bayestestR::estimate_density(method = "KernSmooth") %>% 
    plot() + 
      xlab("Score") + ylab("Frequency") +
    see::theme_modern())
}

Read data

Behav <- read.csv('BehaviouraldataNEW.csv', stringsAsFactors = FALSE, header = TRUE)[-c(1)] %>% 
  rename(participant = ID,
         truthfulness = condition) %>% 
  mutate(ConditionSP = rep(c("social","polygraph"), each = 40, times = 30))

Behav[stringr::str_detect(names(Behav), "subresp")] <- effectsize::change_scale(Behav[stringr::str_detect(names(Behav), "subresp")], from = c(0, 1), to = c(10, -10))

Phy <- read.csv('DT_04.csv', stringsAsFactors = FALSE, header = TRUE) [-c(1),] %>% 
  rename(participant = Participant.number,) %>% 
  mutate(Trial = rep(1:80, each = 4, times = 30)) 

data <- merge(Behav, Phy, by = c("participant" ,"Trial"), all = TRUE) %>% 
  arrange(participant,Trial) %>% 
  mutate(participant = as.factor(participant),
         ConditionSP = as.factor(ConditionSP))

Demo <- read.csv('demo_30.csv', stringsAsFactors = FALSE, header = TRUE)[-c(2,3,4:6,9,11,12,18)]

Descriptive Statistics

Participants Demographics Information

Summary of participants Gender, Age, and Handedness

#General Descriptive Stats
report(Demo)
## The data contains 30 observations of the following variables:
##   - X: n = 30, Mean = 15.50, SD = 8.80, Median = 15.50, MAD = 11.12, range: [1, 30], Skewness = 0.00, Kurtosis = -1.20, 0 missing
##   - Gender: 2 entries, such as Female (n = 19); Male (n = 11); NA (0 missing)
##   - Age: n = 30, Mean = 21.10, SD = 2.09, Median = 20.50, MAD = 2.22, range: [18, 25], Skewness = 0.73, Kurtosis = -0.67, 0 missing
##   - handedness: 2 entries, such as Right handed (n = 28); Left handed (n = 2); NA (0 missing)
##   - Polyjc: 5 entries, such as Junior College (n = 18); Polytechnic (n = 9); Equivalent of Junior College in India (n = 1) and 2 others (0 missing)
##   - Ethnicity: 6 entries, such as Chinese (n = 25); Burmese (n = 1); Caucasian (n = 1) and 3 others (0 missing)
##   - Religion: 7 entries, such as Buddhism (n = 10); Not religious (n = 9); Christianity (n = 7) and 4 others (0 missing)
##   - Nationality: 5 entries, such as Singaporean (n = 25); China (n = 2); Indian (n = 1) and 2 others (0 missing)
##   - RaisedinSG: 2 entries, such as Yes (n = 21); No (n = 9); NA (0 missing)
display(Demo)
X Gender Age handedness Polyjc Ethnicity Religion Nationality RaisedinSG
1 Female 20 Right handed Junior College Chinese Not religious Singaporean Yes
2 Female 20 Right handed Polytechnic Chinese Buddhism Singaporean Yes
3 Female 21 Right handed Junior College Chinese Buddhism Singaporean Yes
4 Male 24 Right handed Junior College Chinese Christianity Singaporean Yes
5 Female 19 Left handed Junior College Chinese Buddhism Singaporean Yes
6 Female 22 Right handed Junior College Chinese Christianity Singaporean Yes
7 Male 24 Right handed Polytechnic Chinese Buddhism Singaporean Yes
8 Female 20 Right handed Junior College Chinese Buddhism Malaysian No
9 Female 20 Left handed High school in China Chinese Not religious China No
10 Female 20 Right handed Junior College Chinese Buddhism Singaporean Yes
11 Male 21 Right handed Junior College Burmese Buddhism Singaporean No
12 Male 25 Right handed Polytechnic Chinese Not religious Singaporean Yes
13 Male 22 Right handed Junior College Chinese Christianity Singaporean Yes
14 Female 21 Right handed Polytechnic Malay Islam Singaporean No
15 Male 24 Right handed Polytechnic Chinese Buddhism Singaporean Yes
16 Male 19 Right handed Senior Middle School Chinese Not religious China No
17 Female 21 Right handed Polytechnic Chinese Not religious Singaporean Yes
18 Female 20 Right handed Junior College Chinese Not religious Singaporean Yes
19 Female 19 Right handed Junior College Chinese Not religious Singaporean Yes
20 Female 19 Right handed Junior College Chinese Christianity Singaporean Yes
21 Female 20 Right handed Junior College Caucasian Not religious Singaporean Yes
22 Male 24 Right handed Polytechnic Chinese Buddhism Singaporean Yes
23 Male 25 Right handed Polytechnic Chinese agnostic Singaporean Yes
24 Female 19 Right handed Junior College Chinese Buddhism Singaporean Yes
25 Female 19 Right handed Junior College Chinese Christianity Singaporean No
26 Female 20 Right handed Equivalent of Junior College in India Indian Hinduism Indian No
27 Female 18 Right handed Junior College Chinese Christianity Indonesian No
28 Female 21 Right handed Junior College Pakistani Not religious Singaporean Yes
29 Male 21 Right handed Junior College Chinese Catholicism Singaporean Yes
30 Male 25 Right handed Polytechnic Chinese Christianity Singaporean No 
#Gender
Gender <- Demo %>% 
  group_by(Gender) %>% 
  summarize(n = n(),
            Ratio = n() / nrow(Demo))
report(Demo$Gender)
## x: 2 entries, such as Female (n = 19); Male (n = 11); NA (0 missing)
as.data.frame(table(Demo$Gender)) %>%
  ggplot(aes(x="", y =Freq, fill = reorder(Var1, -Freq))) +
  geom_bar(width = 1, stat = "identity") +
  labs(fill = "Gender") +
  coord_polar("y", start = 0, direction = -1) +
  scale_fill_brewer(palette="Blues") +
  theme_void() +
  theme(legend.text = element_text(size = 12)) +
  theme(legend.title = element_text(face = "bold", size = 20))

#Age
Age <- Demo [-c(1),] %>% 
  summarise(mean(Age),sd(Age))
report(Demo$Age)
## x: n = 30, Mean = 21.10, SD = 2.09, Median = 20.50, MAD = 2.22, range: [18, 25], Skewness = 0.73, Kurtosis = -0.67, 0 missing
as.data.frame(table(Demo$Age)) %>%
  ggplot(aes(x="", y =Freq, fill = reorder(Var1, -Freq))) +
  geom_bar(width = 1, stat = "identity") +
  labs(fill = "Age") +
  coord_polar("y", start = 0, direction = 1) +
  scale_fill_brewer(palette="Oranges", breaks=c("18","19","20","21","22","24","25")) +
  theme_void() +
  theme(legend.text = element_text(size = 15)) +
  theme(legend.title = element_text(face = "bold", size = 20))

#Handedness
Handedness <- Demo %>% 
  group_by(handedness) %>% 
  summarize(n = n(),
            Ratio = n() / nrow(Demo))
report(Demo$handedness)
## x: 2 entries, such as Right handed (n = 28); Left handed (n = 2); NA (0 missing)
as.data.frame(table(Demo$handedness)) %>%
  ggplot(aes(x="", y =Freq, fill = reorder(Var1, Freq))) +
  geom_bar(width = 1, stat = "identity") +
  labs(fill = "Handedness") +
  coord_polar("y", start = 0, direction = -1) +
  scale_fill_brewer(palette="greens") +
  theme_void() +
  theme(legend.text = element_text(size = 12)) +
  theme(legend.title = element_text(face = "bold", size = 20))

#Ethnicity
as.data.frame(table(Demo$Ethnicity)) %>%
  ggplot(aes(x="", y =Freq, fill = reorder(Var1, -Freq))) +
  geom_bar(width = 1, stat = "identity") +
  labs(fill = "Ethnicity") +
  coord_polar("y", start = 0, direction = -1) +
  scale_fill_brewer(palette="Pastel1") +
  theme_void() +
  theme(legend.text = element_text(size = 12)) +
  theme(legend.title = element_text(face = "bold", size = 20))

#Religion
as.data.frame(table(Demo$Religion)) %>%
  ggplot(aes(x="", y =Freq, fill = reorder(Var1, -Freq))) +
  geom_bar(width = 1, stat = "identity") +
  labs(fill = "Religion") +
  coord_polar("y", start = 0, direction = -1) +
  scale_fill_brewer(palette="Set2") +
  theme_void() +
  theme(legend.text = element_text(size = 12)) +
  theme(legend.title = element_text(face = "bold", size = 20))

#Nationality
as.data.frame(table(Demo$Nationality)) %>%
  ggplot(aes(x="", y =Freq, fill = reorder(Var1, -Freq))) +
  geom_bar(width = 1, stat = "identity") +
  labs(fill = "Nationality") +
  coord_polar("y", start = 0, direction = -1) +
  scale_fill_brewer(palette="Set2") +
  theme_void() +
  theme(legend.text = element_text(size = 12)) +
  theme(legend.title = element_text(face = "bold", size = 20))

Tasks Descriptive Statistics

Summary of Questionnaires and Behavioural Tasks Data

des_stat <- data %>% 
group_by(participant) %>% 
  summarise_all(mean) %>% 
  mutate(yoni_new = yoni_affective + yoni_cognitive) %>% 
  select(-ends_with("Total"),
          -ends_with("TotalAvg"))

#Questionnaires
descriptive_statistics(des_stat, "BES_")          #Basic Empathy/ToM
## Variable      |  Mean |   SD |   Min |   Max | Skewness | Kurtosis
## ------------------------------------------------------------------
## BES_Cognitive | 35.40 | 4.28 | 26.00 | 44.00 |    -0.40 |     0.12
## BES_Affective | 38.03 | 7.13 | 25.00 | 53.00 |     0.19 |    -0.50

descriptive_statistics(des_stat, "MAIA")          #Interoceptive Awareness
## Variable | Mean |   SD |  Min |  Max | Skewness | Kurtosis
## ----------------------------------------------------------
## MAIA1    | 2.98 | 0.70 | 0.75 | 4.25 |    -0.94 |     2.43
## MAIA2    | 1.76 | 0.90 | 0.17 | 3.67 |     0.08 |    -0.12
## MAIA3    | 2.14 | 0.77 | 0.60 | 3.80 |     0.25 |    -0.58
## MAIA4    | 2.72 | 0.91 | 0.71 | 4.14 |    -0.37 |    -0.34
## MAIA5    | 3.33 | 0.77 | 1.40 | 4.80 |    -0.51 |     0.02
## MAIA6    | 2.83 | 0.81 | 1.50 | 4.25 |     0.07 |    -1.33
## MAIA7    | 2.56 | 1.13 | 0.33 | 5.00 |     0.04 |    -0.67
## MAIA8    | 3.36 | 1.06 | 0.33 | 5.00 |    -0.99 |     0.95

descriptive_statistics(des_stat, "SRP3_")         #Psychopathic Traits
## Variable |  Mean |    SD |   Min |    Max | Skewness | Kurtosis
## ---------------------------------------------------------------
## SRP3_IPM | 42.30 |  9.35 | 25.00 |  63.00 |    -0.04 |    -0.25
## SRP3_CA  | 37.87 |  9.62 | 22.00 |  55.00 |     0.21 |    -1.21
## SRP3_ELS | 40.40 |  7.33 | 25.00 |  55.00 |    -0.36 |    -0.14
## SRP3_ASB | 22.50 |  6.61 | 16.00 |  42.00 |     1.46 |     2.10
## SRP3_PRI | 80.17 | 16.73 | 51.00 | 113.00 |    -0.02 |    -0.64
## SRP3_SEC | 62.90 | 11.83 | 42.00 |  90.00 |     0.22 |    -0.16

#Behavioural Tasks
descriptive_statistics(des_stat, "HCT") +
  facet_wrap(~Parameter, scales="free")           #Heartbeat Counting Task (Interoception)
## Variable       | Mean |   SD |   Min |  Max | Skewness | Kurtosis
## -----------------------------------------------------------------
## HCT_Confidence | 0.46 | 0.30 |  0.00 | 0.89 |    -0.16 |    -1.41
## HCT_Accuracy   | 0.93 | 0.06 |  0.76 | 0.99 |    -1.34 |     1.11
## HCT_Awareness  | 0.10 | 0.55 | -0.93 | 0.98 |     0.03 |    -0.75

descriptive_statistics(des_stat, "yoni_")         #Yoni Task (ToM)
## Variable       |  Mean |   SD |   Min |   Max | Skewness | Kurtosis
## -------------------------------------------------------------------
## yoni_affective | 41.83 | 4.07 | 33.00 | 47.00 |    -0.79 |    -0.39
## yoni_cognitive | 31.03 | 3.06 | 24.00 | 36.00 |    -0.50 |     0.04
## yoni_physical  | 12.20 | 2.51 |  4.00 | 14.00 |    -1.85 |     3.11
## yoni_new       | 72.87 | 6.60 | 58.00 | 83.00 |    -0.62 |    -0.52

#Deception Task Subjective Response Data
df_sub <- Behav %>% 
  filter(truthfulness == "LIE") %>% 
  group_by(participant, ConditionSP) %>% 
  summarise_all(mean) %>% 
  select(1,2,9) %>% 
  pivot_wider(names_from = ConditionSP, values_from = subresp) %>% 
  rename(sub_poly = polygraph,
         sub_soc = social) %>% 
  mutate(participant = as.factor(participant)) %>% 
  mutate(sub_total = (sub_poly + sub_soc)/2) %>% 
  ungroup(participant)

descriptive_statistics(df_sub, "sub_")          #Subjective Response
## Variable  | Mean |   SD |   Min |   Max | Skewness | Kurtosis
## -------------------------------------------------------------
## sub_poly  | 2.75 | 5.31 | -5.84 | 10.00 |     0.14 |    -1.40
## sub_soc   | 2.60 | 4.54 | -6.25 | 10.00 |     0.34 |    -0.69
## sub_total | 2.68 | 4.72 | -6.05 | 10.00 |     0.27 |    -1.06

Participants Individual Summary Results for Behavioural Tasks (Deception, HCT & Yoni Task)

df_descriptive <- data %>% 
  filter(truthfulness != "TRUTH") %>% 
  group_by(participant, ConditionSP) %>% 
  summarise_all(mean)

df_descriptive <- df_descriptive %>% 
  select(c(1,2,9,36:42)) %>% 
  pivot_wider(names_from = ConditionSP, values_from = subresp) %>% 
  rename(PolyCon_subresp = polygraph,
         SociCon_subresp = social)
display(df_descriptive)
participant yoni_affective yoni_cognitive yoni_physical yoni_total HCT_Confidence HCT_Accuracy HCT_Awareness PolyCon_subresp SociCon_subresp
1 45 31 14 90 0.36 0.98 -0.29 1.25 2.67
2 44 34 12 90 0.17 0.92 0.28 8.67 7.37
3 33 28 10 71 0.00 0.96 10.00 10.00
4 45 34 13 92 0.75 0.87 -0.32 -2.15 -3.86
5 36 28 12 76 0.58 0.89 -0.45 -0.11 -1.19
6 41 30 13 84 0.89 0.76 0.02 8.91 7.45
7 45 29 11 85 0.58 0.98 -0.44 -2.79 -2.21
8 44 35 14 93 0.24 0.98 -0.21 2.67 -0.54
9 34 30 14 78 0.55 0.96 0.18 9.99 9.99
10 41 31 13 85 0.67 0.97 7.26e-03 -3.12 0.08
11 44 34 10 88 0.53 0.92 -0.76 2.49 2.51
12 34 24 4 62 0.86 0.99 0.87 -0.19 -1.06
13 41 29 14 84 0.10 0.78 0.89 4.99 3.98
14 42 29 7 78 0.13 0.94 0.70 -2.53 2.17
15 46 33 14 93 0.81 0.93 0.91 10.00 10.00
16 45 32 13 90 0.65 0.98 0.03 1.59 2.28
17 44 29 14 87 0.25 0.91 -0.78 -4.13 -0.97
18 43 35 13 91 0.43 0.94 0.03 9.99 1.30
19 37 31 13 81 0.00 0.93 10.00 10.00
20 47 36 13 96 0.40 0.98 0.26 3.86 3.54
21 45 34 14 93 0.17 0.95 0.48 -0.55 -1.44
22 41 30 14 85 0.77 0.96 -0.22 7.42 7.41
23 46 35 13 94 0.00 0.95 10.00 10.00
24 41 33 14 88 0.11 0.86 -0.93 8.56 0.67
25 47 33 14 94 0.67 0.84 -0.12 -2.86 -2.31
26 38 24 14 76 0.75 0.95 0.98 1.09 0.34
27 46 32 13 91 0.18 0.80 0.53 3.59 3.47
28 37 28 8 73 0.57 0.96 0.08 -1.02 1.25
29 40 29 13 82 0.87 0.99 0.94 -4.97 1.55
30 43 31 8 82 0.82 0.92 0.11 -5.84 -6.12

Summary of Tasks

average and SD of each task

Task_summary <- data.frame(
  Mean = sapply(df_descriptive[2:ncol(df_descriptive)], mean, na.rm = TRUE),
  SD = sapply(df_descriptive[2:ncol(df_descriptive)], sd, na.rm = TRUE)
)
print(Task_summary)
##                       Mean         SD
## yoni_affective  41.8333333 4.06908728
## yoni_cognitive  31.0333333 3.05674306
## yoni_physical   12.2000000 2.51066690
## yoni_total      85.0666667 8.00402198
## HCT_Confidence   0.4627901 0.29733940
## HCT_Accuracy     0.9251062 0.06122301
## HCT_Awareness    0.1024937 0.55041888
## PolyCon_subresp  2.8264573 5.34374430
## SociCon_subresp  2.6107131 4.54323359

Correlation Matrix of all Variables

df_correlation <- data %>% 
  filter(truthfulness != "TRUTH") %>% 
  filter(Condition == "R") %>% 
  select(c(1,8,50,55:90)) %>% 
  mutate(participant = as.factor(participant),
         ConditionSP = as.factor(ConditionSP))

Corr_Matrix <- correlation::correlation(
  select(df_correlation,
         participant, 
         ConditionSP, 
         subresp, 
         starts_with("ECG"), 
         starts_with("RSP"),
         starts_with("EDA")),
  multilevel = F, p_adjust = "none") 

dplyr::filter(Corr_Matrix, Parameter1 == "subresp") %>% 
  arrange(desc(abs(r)))
Parameter1 Parameter2 r CI CI_low CI_high t df_error p Method n_Obs
subresp ECG_Rate_Trend_R2 -0.1275993 0.95 -0.1834493 -0.0709282 -4.4061777 1173 0.0000115 Pearson correlation 1175
subresp ECG_Rate_Max -0.1199388 0.95 -0.1759209 -0.0631835 -4.1376647 1173 0.0000376 Pearson correlation 1175
subresp RSP_Amplitude_Mean -0.1101241 0.95 -0.1662657 -0.0532709 -3.7947317 1173 0.0001554 Pearson correlation 1175
subresp ECG_Rate_Max_Time -0.1057522 0.95 -0.1619613 -0.0488590 -3.6423398 1173 0.0002820 Pearson correlation 1175
subresp RSP_Amplitude_Max -0.0923887 0.95 -0.1487912 -0.0353870 -3.1778203 1173 0.0015226 Pearson correlation 1175
subresp ECG_Rate_Min 0.0837259 0.95 0.0266649 0.1402431 2.8776409 1173 0.0040792 Pearson correlation 1175
subresp ECG_Rate_Min_Time 0.0753114 0.95 0.0182011 0.1319319 2.5866929 1173 0.0098097 Pearson correlation 1175
subresp RSP_Rate_Baseline -0.0631673 0.95 -0.1199228 -0.0060003 -2.1677522 1173 0.0303782 Pearson correlation 1175
subresp ECG_Rate_Trend_Quadratic -0.0538695 0.95 -0.1107171 0.0033295 -1.8476636 1173 0.0649027 Pearson correlation 1175
subresp ECG_Phase_Completion_Atrial 0.0516054 0.95 -0.0055998 0.1084740 1.7697961 1173 0.0770209 Pearson correlation 1175
subresp EDA_SCR 0.0514792 0.95 -0.0057264 0.1083489 1.7654553 1173 0.0777474 Pearson correlation 1175
subresp RSP_Rate_Trend_R2 -0.0420333 0.95 -0.0989840 0.0151919 -1.4408754 1173 0.1498868 Pearson correlation 1175
subresp ECG_Rate_Trend_Linear 0.0351158 0.95 -0.0221173 0.0921195 1.2034265 1173 0.2290541 Pearson correlation 1175
subresp RSP_Amplitude_Min -0.0348684 0.95 -0.0918739 0.0223649 -1.1949364 1173 0.2323534 Pearson correlation 1175
subresp ECG_Phase_Ventricular -0.0337712 0.95 -0.0907846 0.0234628 -1.1572945 1173 0.2473877 Pearson correlation 1175
subresp RSP_Rate_Min 0.0331595 0.95 -0.0240748 0.0901772 1.1363081 1173 0.2560597 Pearson correlation 1175
subresp RSP_Rate_Trend_Linear 0.0286505 0.95 -0.0285850 0.0856988 0.9816570 1173 0.3264713 Pearson correlation 1175
subresp RSP_Rate_Trend_Quadratic -0.0235461 0.95 -0.0806262 0.0336880 -0.8066552 1173 0.4200286 Pearson correlation 1175
subresp ECG_Phase_Completion_Ventricular -0.0197867 0.95 -0.0768884 0.0374444 -0.6778091 1173 0.4980264 Pearson correlation 1175
subresp RSP_Rate_Mean 0.0184268 0.95 -0.0388028 0.0755359 0.6312083 1173 0.5280273 Pearson correlation 1175
subresp RSP_Rate_Min_Time -0.0164955 0.95 -0.0736148 0.0407316 -0.5650340 1173 0.5721587 Pearson correlation 1175
subresp ECG_Rate_Mean 0.0155679 0.95 -0.0416579 0.0726919 0.5332507 1173 0.5939610 Pearson correlation 1175
subresp RSP_Phase -0.0148613 0.95 -0.0719888 0.0423634 -0.5090426 1173 0.6108181 Pearson correlation 1175
subresp RSP_Phase_Completion 0.0126839 0.95 -0.0445371 0.0698219 0.4344453 1173 0.6640450 Pearson correlation 1175
subresp RSP_Rate_Max -0.0122189 0.95 -0.0693591 0.0450012 -0.4185182 1173 0.6756449 Pearson correlation 1175
subresp RSP_Rate_Max_Time -0.0117759 0.95 -0.0689182 0.0454434 -0.4033426 1173 0.6867697 Pearson correlation 1175
subresp ECG_Rate_Baseline 0.0117109 0.95 -0.0455083 0.0688535 0.4011149 1173 0.6884086 Pearson correlation 1175
subresp ECG_Quality_Mean -0.0104587 0.95 -0.0676069 0.0467580 -0.3582194 1173 0.7202436 Pearson correlation 1175
subresp EDA_Peak_Amplitude -0.0042894 0.95 -0.0614630 0.0529123 -0.1469079 1173 0.8832300 Pearson correlation 1175
subresp ECG_Phase_Atrial 0.0007035 0.95 -0.0564874 0.0578899 0.0240957 1173 0.9807803 Pearson correlation 1175

Hypothesis Analysis

Subjective Response Comparison

Linear Mixed Model Regression

Hypothesis 1A: Predicted Values of Subjective Response by Yoni Task

df <- data %>% 
  mutate(participant = as.factor(participant),
         ConditionSP = as.factor(ConditionSP)) %>% 
  filter(truthfulness == "LIE") %>% 
  filter(Inclusion2 != "0") %>% 
  filter(participant != "3",
         participant != "15",
         participant != "19",
         participant != "23") %>% 
  filter(Condition == "Q") %>% 
  mutate(yoni_new = yoni_affective + yoni_cognitive) 
df$ConditionSP <- relevel(df$ConditionSP, ref = "social")

# TOM Hypothesis 1A: SUBRESP -> YONI
model_sub1 <- lme4::lmer(subresp ~ ConditionSP * yoni_new + (1|participant), data = df)  #explorative
parameters::parameters(model_sub1)
Parameter Coefficient SE CI CI_low CI_high t df_error p Effects Group
(Intercept) 6.8528204 9.8681562 0.95 -12.4884103 26.1940510 0.6944378 948 0.4874077 fixed
ConditionSPpolygraph -8.3898130 3.9530534 0.95 -16.1376553 -0.6419706 -2.1223627 948 0.0338073 fixed
yoni_new -0.0751755 0.1347756 0.95 -0.3393307 0.1889797 -0.5577828 948 0.5769927 fixed
ConditionSPpolygraph:yoni_new 0.1193514 0.0540438 0.95 0.0134274 0.2252753 2.2084178 948 0.0272152 fixed
SD (Intercept) 4.0785473 NA 0.95 NA NA NA NA NA random participant
SD (Observations) 2.2695234 NA 0.95 NA NA NA NA NA random Residual 
plot_model(model_sub1, type = "pred", terms = c("yoni_new", "ConditionSP"), axis.title = c("Yoni Task", "Subjective Response"), title = "Predicted Values of Subjective Response by Yoni Task", legend.title = "Condition") + theme_sjplot(base_size = 10, base_family = "Times New Roman")

report(model_sub1)
## We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict subresp with ConditionSP and yoni_new (formula: subresp ~ ConditionSP * yoni_new). The model included participant as random effect (formula: ~1 | participant). The model's total explanatory power is substantial (conditional R2 = 0.39) and the part related to the fixed effects alone (marginal R2) is of 3.92e-03. The model's intercept, corresponding to ConditionSP = social and yoni_new = 0, is at 6.85 (95% CI [-12.49, 26.19], t(948) = 0.69, p = 0.487). Within this model:
## 
##   - The effect of ConditionSP [polygraph] is statistically significant and negative (beta = -8.39, 95% CI [-16.14, -0.64], t(948) = -2.12, p < .05; Std. beta = 0.05, 95% CI [-0.05, 0.15])
##   - The effect of yoni_new is statistically non-significant and negative (beta = -0.08, 95% CI [-0.34, 0.19], t(948) = -0.56, p = 0.577; Std. beta = -0.07, 95% CI [-0.32, 0.18])
##   - The interaction effect of yoni_new on ConditionSP [polygraph] is statistically significant and positive (beta = 0.12, 95% CI [0.01, 0.23], t(948) = 2.21, p < .05; Std. beta = 0.11, 95% CI [0.01, 0.22])
##   - The effect of ConditionSP [polygraph] is statistically significant and negative (beta = -8.39, 95% CI [-16.14, -0.64], t(948) = -2.12, p < .05; Std. beta = 0.89, )
## 
## Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using the Wald approximation.

Hypothesis 1A: Predicted Values of Subjective Response by BES Questionnaire

# TOM Hypothesis 1A: SUBRESP -> YONI/BES
model_sub2 <- lme4::lmer(subresp ~ ConditionSP * BES_Total + (1|participant), data = df)
parameters::parameters(model_sub2)
Parameter Coefficient SE CI CI_low CI_high t df_error p Effects Group
(Intercept) 3.3794418 6.9891128 0.95 -10.3189676 17.0778513 0.4835294 948 0.6287199 fixed
ConditionSPpolygraph -7.4937624 2.8359518 0.95 -13.0521257 -1.9353991 -2.6424153 948 0.0082317 fixed
BES_Total -0.0275047 0.0948164 0.95 -0.2133415 0.1583321 -0.2900839 948 0.7717521 fixed
ConditionSPpolygraph:BES_Total 0.1068273 0.0385568 0.95 0.0312572 0.1823973 2.7706434 948 0.0055946 fixed
SD (Intercept) 4.0810687 NA 0.95 NA NA NA NA NA random participant
SD (Observations) 2.2677728 NA 0.95 NA NA NA NA NA random Residual 
plot_model(model_sub2, type = "pred", terms = c("BES_Total", "ConditionSP"), axis.title = c("BES Questionnaire", "Subjective Response"), title = "Predicted Values of Subjective Response by BES Questionnaire", legend.title = "Condition") + theme_sjplot(base_size = 10, base_family = "Times New Roman")

report(model_sub2)
## We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict subresp with ConditionSP and BES_Total (formula: subresp ~ ConditionSP * BES_Total). The model included participant as random effect (formula: ~1 | participant). The model's total explanatory power is substantial (conditional R2 = 0.39) and the part related to the fixed effects alone (marginal R2) is of 6.50e-03. The model's intercept, corresponding to ConditionSP = social and BES_Total = 0, is at 3.38 (95% CI [-10.32, 17.08], t(948) = 0.48, p = 0.629). Within this model:
## 
##   - The effect of ConditionSP [polygraph] is statistically significant and negative (beta = -7.49, 95% CI [-13.05, -1.94], t(948) = -2.64, p < .01; Std. beta = 0.05, 95% CI [-0.05, 0.15])
##   - The effect of BES_Total is statistically non-significant and negative (beta = -0.03, 95% CI [-0.21, 0.16], t(948) = -0.29, p = 0.772; Std. beta = -0.04, 95% CI [-0.29, 0.21])
##   - The interaction effect of BES_Total on ConditionSP [polygraph] is statistically significant and positive (beta = 0.11, 95% CI [0.03, 0.18], t(948) = 2.77, p < .01; Std. beta = 0.14, 95% CI [0.04, 0.24])
##   - The effect of ConditionSP [polygraph] is statistically significant and negative (beta = -7.49, 95% CI [-13.05, -1.94], t(948) = -2.64, p < .01; Std. beta = 0.89, )
## 
## Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using the Wald approximation.

Hypothesis 1C: Predicted Values of Subjective Response by SRP3

#TOM Hypothesis 1C: SUBRESP -> SRP3
model_sub3 <- lme4::lmer(subresp ~ ConditionSP * SRP3_Total + (1|participant), data = df)
parameters::parameters(model_sub3)
Parameter Coefficient SE CI CI_low CI_high t df_error p Effects Group
(Intercept) -4.0315924 4.5094050 0.95 -12.8698637 4.8066789 -0.8940409 948 0.3713000 fixed
ConditionSPpolygraph 0.9748765 1.8454061 0.95 -2.6420530 4.5918059 0.5282721 948 0.5973105 fixed
SRP3_Total 0.0383807 0.0315086 0.95 -0.0233749 0.1001364 1.2181040 948 0.2231844 fixed
ConditionSPpolygraph:SRP3_Total -0.0047447 0.0129171 0.95 -0.0300617 0.0205724 -0.3673178 948 0.7133820 fixed
SD (Intercept) 3.9616800 NA 0.95 NA NA NA NA NA random participant
SD (Observations) 2.2724345 NA 0.95 NA NA NA NA NA random Residual 
plot_model(model_sub3, type = "pred", terms = c("SRP3_Total", "ConditionSP"), axis.title = c("SRP3 Inventory", "Subjective Response"), title = "Predicted Values of Subjective Response by SRP3 Inventory", legend.title = "Condition") + theme_sjplot(base_size = 10, base_family = "Times New Roman")

report(model_sub3)
## We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict subresp with ConditionSP and SRP3_Total (formula: subresp ~ ConditionSP * SRP3_Total). The model included participant as random effect (formula: ~1 | participant). The model's total explanatory power is substantial (conditional R2 = 0.38) and the part related to the fixed effects alone (marginal R2) is of 0.02. The model's intercept, corresponding to ConditionSP = social and SRP3_Total = 0, is at -4.03 (95% CI [-12.87, 4.81], t(948) = -0.89, p = 0.371). Within this model:
## 
##   - The effect of ConditionSP [polygraph] is statistically non-significant and positive (beta = 0.97, 95% CI [-2.64, 4.59], t(948) = 0.53, p = 0.597; Std. beta = 0.05, 95% CI [-0.05, 0.15])
##   - The effect of SRP3_Total is statistically non-significant and positive (beta = 0.04, 95% CI [-0.02, 0.10], t(948) = 1.22, p = 0.223; Std. beta = 0.15, 95% CI [-0.09, 0.40])
##   - The interaction effect of SRP3_Total on ConditionSP [polygraph] is statistically non-significant and negative (beta = -4.74e-03, 95% CI [-0.03, 0.02], t(948) = -0.37, p = 0.713; Std. beta = -0.02, 95% CI [-0.12, 0.08])
##   - The effect of ConditionSP [polygraph] is statistically non-significant and positive (beta = 0.97, 95% CI [-2.64, 4.59], t(948) = 0.53, p = 0.597; Std. beta = 0.89, )
## 
## Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using the Wald approximation.

Hypothesis 2A: Predicted Values of Subjective Response by HCT Accuracy

df$ConditionSP <- relevel(df$ConditionSP, ref = "polygraph")

#Interoception Hypothesis 2A: SUBRESP -> HCT
model_sub4 <- lme4::lmer(subresp ~ ConditionSP * HCT_Accuracy + (1|participant), data = df)
parameters::parameters(model_sub4)
Parameter Coefficient SE CI CI_low CI_high t df_error p Effects Group
(Intercept) 18.488796 11.851499 0.95 -4.739715 41.717308 1.560039 948 0.1187508 fixed
ConditionSPsocial -12.414708 4.823371 0.95 -21.868340 -2.961075 -2.573866 948 0.0100569 fixed
HCT_Accuracy -18.223889 12.817837 0.95 -43.346387 6.898610 -1.421760 948 0.1550959 fixed
ConditionSPsocial:HCT_Accuracy 13.128547 5.218345 0.95 2.900778 23.356316 2.515845 948 0.0118747 fixed
SD (Intercept) 4.010104 NA 0.95 NA NA NA NA NA random participant
SD (Observations) 2.268615 NA 0.95 NA NA NA NA NA random Residual 
plot_model(model_sub4, type = "pred", terms = c("HCT_Accuracy", "ConditionSP"), axis.title = c("HCT Accuracy", "Subjective Response"), title = "Predicted Values of Subjective Response by HCT Accuracy", legend.title = "Condition") + theme_sjplot(base_size = 10, base_family = "Times New Roman")

report(model_sub4)
## We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict subresp with ConditionSP and HCT_Accuracy (formula: subresp ~ ConditionSP * HCT_Accuracy). The model included participant as random effect (formula: ~1 | participant). The model's total explanatory power is substantial (conditional R2 = 0.39) and the part related to the fixed effects alone (marginal R2) is of 0.02. The model's intercept, corresponding to ConditionSP = polygraph and HCT_Accuracy = 0, is at 18.49 (95% CI [-4.74, 41.72], t(948) = 1.56, p = 0.119). Within this model:
## 
##   - The effect of ConditionSP [social] is statistically significant and negative (beta = -12.41, 95% CI [-21.87, -2.96], t(948) = -2.57, p < .05; Std. beta = -0.05, 95% CI [-0.15, 0.05])
##   - The effect of HCT_Accuracy is statistically non-significant and negative (beta = -18.22, 95% CI [-43.35, 6.90], t(948) = -1.42, p = 0.155; Std. beta = -0.18, 95% CI [-0.43, 0.07])
##   - The interaction effect of HCT_Accuracy on ConditionSP [social] is statistically significant and positive (beta = 13.13, 95% CI [2.90, 23.36], t(948) = 2.52, p < .05; Std. beta = 0.13, 95% CI [0.03, 0.23])
##   - The effect of ConditionSP [social] is statistically significant and negative (beta = -12.41, 95% CI [-21.87, -2.96], t(948) = -2.57, p < .05; Std. beta = 0.89, )
## 
## Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using the Wald approximation.

Hypothesis 2A: Predicted Values of Subjective Response by MAIA2

#Interoception Hypothesis 2A: SUBRESP -> MAIA2
model_sub5 <- lme4::lmer(subresp ~ ConditionSP * MAIA_Total + (1|participant), data = df)
parameters::parameters(model_sub5)
Parameter Coefficient SE CI CI_low CI_high t df_error p Effects Group
(Intercept) 8.582359 4.0502482 0.95 0.6440189 16.5206999 2.118971 948 0.0340929 fixed
ConditionSPsocial -3.249959 1.6862691 0.95 -6.5549858 0.0550675 -1.927308 948 0.0539413 fixed
MAIA_Total -2.580371 1.4838168 0.95 -5.4885989 0.3278560 -1.739010 948 0.0820331 fixed
ConditionSPsocial:MAIA_Total 1.101971 0.6193228 0.95 -0.1118797 2.3158209 1.779316 948 0.0751880 fixed
SD (Intercept) 3.908004 NA 0.95 NA NA NA NA NA random participant
SD (Observations) 2.270618 NA 0.95 NA NA NA NA NA random Residual 
plot_model(model_sub5, type = "pred", terms = c("MAIA_Total", "ConditionSP"), axis.title = c("MAIA2", "Subjective Response"), title = "Predicted Values of Subjective Response by MAIA2", legend.title = "Condition") + theme_sjplot(base_size = 10, base_family = "Times New Roman")

report(model_sub5)
## We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict subresp with ConditionSP and MAIA_Total (formula: subresp ~ ConditionSP * MAIA_Total). The model included participant as random effect (formula: ~1 | participant). The model's total explanatory power is substantial (conditional R2 = 0.38) and the part related to the fixed effects alone (marginal R2) is of 0.03. The model's intercept, corresponding to ConditionSP = polygraph and MAIA_Total = 0, is at 8.58 (95% CI [0.64, 16.52], t(948) = 2.12, p < .05). Within this model:
## 
##   - The effect of ConditionSP [social] is statistically non-significant and negative (beta = -3.25, 95% CI [-6.55, 0.06], t(948) = -1.93, p = 0.054; Std. beta = -0.05, 95% CI [-0.15, 0.05])
##   - The effect of MAIA_Total is statistically non-significant and negative (beta = -2.58, 95% CI [-5.49, 0.33], t(948) = -1.74, p = 0.082; Std. beta = -0.22, 95% CI [-0.46, 0.03])
##   - The interaction effect of MAIA_Total on ConditionSP [social] is statistically non-significant and positive (beta = 1.10, 95% CI [-0.11, 2.32], t(948) = 1.78, p = 0.075; Std. beta = 0.09, 95% CI [-9.34e-03, 0.19])
##   - The effect of ConditionSP [social] is statistically non-significant and negative (beta = -3.25, 95% CI [-6.55, 0.06], t(948) = -1.93, p = 0.054; Std. beta = 0.89, )
## 
## Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using the Wald approximation.

Hypothesis 2C: Predicted Values of Subjective Response by Primary SRP3 Subfactor

#Interoception Hypothesis 2C: SUBRESP -> SRP3_PRI
model_sub6 <- lme4::lmer(subresp ~ ConditionSP * SRP3_PRI + (1|participant), data = df)
parameters::parameters(model_sub6)
Parameter Coefficient SE CI CI_low CI_high t df_error p Effects Group
(Intercept) -3.2763117 3.9672390 0.95 -11.0519573 4.4993339 -0.8258418 948 0.4088939 fixed
ConditionSPsocial -0.5546161 1.6372337 0.95 -3.7635352 2.6543029 -0.3387520 948 0.7347966 fixed
SRP3_PRI 0.0631348 0.0494939 0.95 -0.0338714 0.1601410 1.2756093 948 0.2020937 fixed
ConditionSPsocial:SRP3_PRI 0.0031487 0.0204673 0.95 -0.0369665 0.0432639 0.1538397 948 0.8777362 fixed
SD (Intercept) 3.9267513 NA 0.95 NA NA NA NA NA random participant
SD (Observations) 2.2725081 NA 0.95 NA NA NA NA NA random Residual 
plot_model(model_sub6, type = "pred", terms = c("SRP3_PRI", "ConditionSP"), axis.title = c("SRP3 (PRI) Inventory", "Subjective Response"), title = "Predicted Values of Subjective Response by SRP3 (PRI)", legend.title = "Condition") + theme_sjplot(base_size = 10, base_family = "Times New Roman")

report(model_sub6)
## We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict subresp with ConditionSP and SRP3_PRI (formula: subresp ~ ConditionSP * SRP3_PRI). The model included participant as random effect (formula: ~1 | participant). The model's total explanatory power is substantial (conditional R2 = 0.38) and the part related to the fixed effects alone (marginal R2) is of 0.03. The model's intercept, corresponding to ConditionSP = polygraph and SRP3_PRI = 0, is at -3.28 (95% CI [-11.05, 4.50], t(948) = -0.83, p = 0.409). Within this model:
## 
##   - The effect of ConditionSP [social] is statistically non-significant and negative (beta = -0.55, 95% CI [-3.76, 2.65], t(948) = -0.34, p = 0.735; Std. beta = -0.05, 95% CI [-0.15, 0.05])
##   - The effect of SRP3_PRI is statistically non-significant and positive (beta = 0.06, 95% CI [-0.03, 0.16], t(948) = 1.28, p = 0.202; Std. beta = 0.16, 95% CI [-0.09, 0.41])
##   - The interaction effect of SRP3_PRI on ConditionSP [social] is statistically non-significant and positive (beta = 3.15e-03, 95% CI [-0.04, 0.04], t(948) = 0.15, p = 0.878; Std. beta = 7.97e-03, 95% CI [-0.09, 0.11])
##   - The effect of ConditionSP [social] is statistically non-significant and negative (beta = -0.55, 95% CI [-3.76, 2.65], t(948) = -0.34, p = 0.735; Std. beta = 0.89, )
## 
## Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using the Wald approximation.

ECG Physio scores comparison

Linear Mixed Model Regression

Hypothesis 1A: Predicted Values of Heartrate Acceleration by Yoni Task

df2 <- data %>% 
  mutate(participant = as.factor(participant),
         ConditionSP = as.factor(ConditionSP)) %>% 
  filter(truthfulness == "LIE") %>% 
  filter(Inclusion2 != "0") %>%
#  filter(participant != "22") %>% 
#  filter(participant != "23") %>%         
  filter(participant != "30") %>% 
  filter(participant != "15") %>% 
  filter(Condition == "Q") %>% 
  mutate(yoni_new = yoni_affective + yoni_cognitive) 
df2$ConditionSP <- relevel(df2$ConditionSP, ref = "social")

#ECG_RATE_MAX
#TOM Hypothesis 1A: ECG_Rate_Max -> YONI
model_ecg1 <- lme4::lmer(ECG_Rate_Max ~ ConditionSP * yoni_new + (1|participant), data = df2)  #explorative
parameters::parameters(model_ecg1)
Parameter Coefficient SE CI CI_low CI_high t df_error p Effects Group
(Intercept) 27.2578083 7.1631515 0.95 13.2182894 41.2973273 3.8052816 1018 0.0001416 fixed
ConditionSPpolygraph -2.1507216 3.6532777 0.95 -9.3110143 5.0095712 -0.5887101 1018 0.5560558 fixed
yoni_new -0.2572554 0.0982664 0.95 -0.4498541 -0.0646567 -2.6179373 1018 0.0088463 fixed
ConditionSPpolygraph:yoni_new -0.0015613 0.0501970 0.95 -0.0999455 0.0968230 -0.0311027 1018 0.9751876 fixed
SD (Intercept) 3.2041857 NA 0.95 NA NA NA NA NA random participant
SD (Observations) 2.2969355 NA 0.95 NA NA NA NA NA random Residual 
plot_model(model_ecg1, type = "pred", terms = c("yoni_new", "ConditionSP"), axis.title = c("Yoni Task", "Heartrate Acceleration"), title = "Predicted Values of Heartrate Acceleration by Yoni Task ", legend.title = "Condition") + theme_sjplot(base_size = 10, base_family = "Times New Roman")

report(model_ecg1)
## We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict ECG_Rate_Max with ConditionSP and yoni_new (formula: ECG_Rate_Max ~ ConditionSP * yoni_new). The model included participant as random effect (formula: ~1 | participant). The model's total explanatory power is substantial (conditional R2 = 0.34) and the part related to the fixed effects alone (marginal R2) is of 0.10. The model's intercept, corresponding to ConditionSP = social and yoni_new = 0, is at 27.26 (95% CI [13.22, 41.30], t(1018) = 3.81, p < .001). Within this model:
## 
##   - The effect of ConditionSP [polygraph] is statistically non-significant and negative (beta = -2.15, 95% CI [-9.31, 5.01], t(1018) = -0.59, p = 0.556; Std. beta = -0.35, 95% CI [-0.45, -0.25])
##   - The effect of yoni_new is statistically significant and negative (beta = -0.26, 95% CI [-0.45, -0.06], t(1018) = -2.62, p < .01; Std. beta = -0.26, 95% CI [-0.46, -0.07])
##   - The interaction effect of yoni_new on ConditionSP [polygraph] is statistically non-significant and negative (beta = -1.56e-03, 95% CI [-0.10, 0.10], t(1018) = -0.03, p = 0.975; Std. beta = -1.60e-03, 95% CI [-0.10, 0.10])
##   - The effect of ConditionSP [polygraph] is statistically non-significant and negative (beta = -2.15, 95% CI [-9.31, 5.01], t(1018) = -0.59, p = 0.556; Std. beta = 0.91, )
## 
## Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using the Wald approximation.

Hypothesis 1A: Predicted Values of Heartrate Acceleration by BES Questionniare

#TOM Hypothesis 1A: ECG_Rate_Max -> BES
model_ecg2 <- lme4::lmer(ECG_Rate_Max ~ ConditionSP * BES_Total + (1|participant), data = df2)
parameters::parameters(model_ecg2)
Parameter Coefficient SE CI CI_low CI_high t df_error p Effects Group
(Intercept) 0.2052575 5.5806691 0.95 -10.7326530 11.1431681 0.0367801 1018 0.9706603 fixed
ConditionSPpolygraph -3.7951969 2.6802022 0.95 -9.0482967 1.4579029 -1.4160114 1018 0.1567722 fixed
BES_Total 0.1138884 0.0752970 0.95 -0.0336909 0.2614677 1.5125233 1018 0.1304008 fixed
ConditionSPpolygraph:BES_Total 0.0208820 0.0361894 0.95 -0.0500480 0.0918119 0.5770187 1018 0.5639268 fixed
SD (Intercept) 3.4646819 NA 0.95 NA NA NA NA NA random participant
SD (Observations) 2.2967229 NA 0.95 NA NA NA NA NA random Residual 
plot_model(model_ecg2, type = "pred", terms = c("BES_Total", "ConditionSP"), axis.title = c("BES Questionnaire", "Heartrate Acceleration"), title = "Predicted Values of Heartrate Acceleration by BES Questionnaire", legend.title = "Condition") + theme_sjplot(base_size = 10, base_family = "Times New Roman")

report(model_ecg2)
## We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict ECG_Rate_Max with ConditionSP and BES_Total (formula: ECG_Rate_Max ~ ConditionSP * BES_Total). The model included participant as random effect (formula: ~1 | participant). The model's total explanatory power is substantial (conditional R2 = 0.34) and the part related to the fixed effects alone (marginal R2) is of 0.06. The model's intercept, corresponding to ConditionSP = social and BES_Total = 0, is at 0.21 (95% CI [-10.73, 11.14], t(1018) = 0.04, p = 0.971). Within this model:
## 
##   - The effect of ConditionSP [polygraph] is statistically non-significant and negative (beta = -3.80, 95% CI [-9.05, 1.46], t(1018) = -1.42, p = 0.157; Std. beta = -0.35, 95% CI [-0.45, -0.25])
##   - The effect of BES_Total is statistically non-significant and positive (beta = 0.11, 95% CI [-0.03, 0.26], t(1018) = 1.51, p = 0.130; Std. beta = 0.16, 95% CI [-0.05, 0.37])
##   - The interaction effect of BES_Total on ConditionSP [polygraph] is statistically non-significant and positive (beta = 0.02, 95% CI [-0.05, 0.09], t(1018) = 0.58, p = 0.564; Std. beta = 0.03, 95% CI [-0.07, 0.13])
##   - The effect of ConditionSP [polygraph] is statistically non-significant and negative (beta = -3.80, 95% CI [-9.05, 1.46], t(1018) = -1.42, p = 0.157; Std. beta = 0.91, )
## 
## Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using the Wald approximation.

Hypothesis 1C: Predicted Values of Heartrate Acceleration by SRP3

#TOM Hypothesis 1C: ECG_Rate_Max -> SRP3
model_ecg3 <- lme4::lmer(ECG_Rate_Max ~ ConditionSP * SRP3_Total + (1|participant), data = df2)
parameters::parameters(model_ecg3)
Parameter Coefficient SE CI CI_low CI_high t df_error p Effects Group
(Intercept) 16.2949842 3.8878843 0.95 8.6748710 23.9150974 4.191222 1018 0.0000277 fixed
ConditionSPpolygraph -6.0812362 1.8276611 0.95 -9.6633862 -2.4990862 -3.327332 1018 0.0008768 fixed
SRP3_Total -0.0537453 0.0266153 0.95 -0.1059103 -0.0015802 -2.019337 1018 0.0434522 fixed
ConditionSPpolygraph:SRP3_Total 0.0266865 0.0125555 0.95 0.0020781 0.0512948 2.125479 1018 0.0335467 fixed
SD (Intercept) 3.4893102 NA 0.95 NA NA NA NA NA random participant
SD (Observations) 2.2943489 NA 0.95 NA NA NA NA NA random Residual 
plot_model(model_ecg3, type = "pred", terms = c("SRP3_Total", "ConditionSP"), axis.title = c("SRP3 Inventory", "Heartrate Acceleration"), title = "Predicted Values of Heartrate Acceleration by SRP3 Inventory", legend.title = "Condition") + theme_sjplot(base_size = 10, base_family = "Times New Roman")

report(model_ecg3)
## We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict ECG_Rate_Max with ConditionSP and SRP3_Total (formula: ECG_Rate_Max ~ ConditionSP * SRP3_Total). The model included participant as random effect (formula: ~1 | participant). The model's total explanatory power is substantial (conditional R2 = 0.35) and the part related to the fixed effects alone (marginal R2) is of 0.06. The model's intercept, corresponding to ConditionSP = social and SRP3_Total = 0, is at 16.29 (95% CI [8.67, 23.92], t(1018) = 4.19, p < .001). Within this model:
## 
##   - The effect of ConditionSP [polygraph] is statistically significant and negative (beta = -6.08, 95% CI [-9.66, -2.50], t(1018) = -3.33, p < .001; Std. beta = -0.35, 95% CI [-0.45, -0.25])
##   - The effect of SRP3_Total is statistically significant and negative (beta = -0.05, 95% CI [-0.11, -1.58e-03], t(1018) = -2.02, p < .05; Std. beta = -0.22, 95% CI [-0.43, -6.47e-03])
##   - The interaction effect of SRP3_Total on ConditionSP [polygraph] is statistically significant and positive (beta = 0.03, 95% CI [2.08e-03, 0.05], t(1018) = 2.13, p < .05; Std. beta = 0.11, 95% CI [8.50e-03, 0.21])
##   - The effect of ConditionSP [polygraph] is statistically significant and negative (beta = -6.08, 95% CI [-9.66, -2.50], t(1018) = -3.33, p < .001; Std. beta = 0.91, )
## 
## Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using the Wald approximation.

Hypothesis 2A: Predicted Values of Heartrate Acceleration by HCT Accuracy

df2$ConditionSP <- relevel(df2$ConditionSP, ref = "social")

#Interoception Hypothesis 2A: ECG_Rate_Max -> HCT
model_ecg4 <- lme4::lmer(ECG_Rate_Max ~ ConditionSP * HCT_Accuracy + (1|participant), data = df2)
parameters::parameters(model_ecg4)
Parameter Coefficient SE CI CI_low CI_high t df_error p Effects Group
(Intercept) 35.956078 9.809649 0.95 16.729519 55.182637 3.665379 1018 0.0002470 fixed
ConditionSPpolygraph -10.158290 4.912066 0.95 -19.785763 -0.530817 -2.068028 1018 0.0386374 fixed
HCT_Accuracy -29.602954 10.583262 0.95 -50.345766 -8.860142 -2.797149 1018 0.0051556 fixed
ConditionSPpolygraph:HCT_Accuracy 8.541931 5.301230 0.95 -1.848289 18.932150 1.611311 1018 0.1071119 fixed
SD (Intercept) 3.265593 NA 0.95 NA NA NA NA NA random participant
SD (Observations) 2.295488 NA 0.95 NA NA NA NA NA random Residual 
plot_model(model_ecg4, type = "pred", terms = c("HCT_Accuracy", "ConditionSP"), axis.title = c("HCT Accuracy", "Heartrate Acceleration"), title = "Predicted Values of Heartrate Acceleration by HCT Accuracy", legend.title = "Condition") + theme_sjplot(base_size = 10, base_family = "Times New Roman")

report(model_ecg4)
## We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict ECG_Rate_Max with ConditionSP and HCT_Accuracy (formula: ECG_Rate_Max ~ ConditionSP * HCT_Accuracy). The model included participant as random effect (formula: ~1 | participant). The model's total explanatory power is substantial (conditional R2 = 0.34) and the part related to the fixed effects alone (marginal R2) is of 0.09. The model's intercept, corresponding to ConditionSP = social and HCT_Accuracy = 0, is at 35.96 (95% CI [16.73, 55.18], t(1018) = 3.67, p < .001). Within this model:
## 
##   - The effect of ConditionSP [polygraph] is statistically significant and negative (beta = -10.16, 95% CI [-19.79, -0.53], t(1018) = -2.07, p < .05; Std. beta = -0.35, 95% CI [-0.45, -0.25])
##   - The effect of HCT_Accuracy is statistically significant and negative (beta = -29.60, 95% CI [-50.35, -8.86], t(1018) = -2.80, p < .01; Std. beta = -0.29, 95% CI [-0.49, -0.09])
##   - The interaction effect of HCT_Accuracy on ConditionSP [polygraph] is statistically non-significant and positive (beta = 8.54, 95% CI [-1.85, 18.93], t(1018) = 1.61, p = 0.107; Std. beta = 0.08, 95% CI [-0.02, 0.18])
##   - The effect of ConditionSP [polygraph] is statistically significant and negative (beta = -10.16, 95% CI [-19.79, -0.53], t(1018) = -2.07, p < .05; Std. beta = 0.91, )
## 
## Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using the Wald approximation.

Hypothesis 2A: Predicted Values of Heartrate Acceleration by MAIA2

#Interoception Hypothesis 2A: ECG_Rate_Max -> MAIA2
model_ecg5 <- lme4::lmer(ECG_Rate_Max ~ ConditionSP * MAIA_Total + (1|participant), data = df2)
parameters::parameters(model_ecg5)
Parameter Coefficient SE CI CI_low CI_high t df_error p Effects Group
(Intercept) 7.6680831 4.0216640 0.95 -0.2142336 15.5503998 1.9066941 1018 0.0565602 fixed
ConditionSPpolygraph -3.6884331 1.8236835 0.95 -7.2627871 -0.1140792 -2.0225182 1018 0.0431228 fixed
MAIA_Total 0.3446661 1.4974227 0.95 -2.5902284 3.2795605 0.2301729 1018 0.8179574 fixed
ConditionSPpolygraph:MAIA_Total 0.5411528 0.6797932 0.95 -0.7912174 1.8735231 0.7960551 1018 0.4260000 fixed
SD (Intercept) 3.6504185 NA 0.95 NA NA NA NA NA random participant
SD (Observations) 2.2965449 NA 0.95 NA NA NA NA NA random Residual 
plot_model(model_ecg5, type = "pred", terms = c("MAIA_Total", "ConditionSP"), axis.title = c("MAIA2", "Heartrate Acceleration"), title = "Predicted Values of Heartrate Acceleration by MAIA2", legend.title = "Condition") + theme_sjplot(base_size = 10, base_family = "Times New Roman")

report(model_ecg5)
## We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict ECG_Rate_Max with ConditionSP and MAIA_Total (formula: ECG_Rate_Max ~ ConditionSP * MAIA_Total). The model included participant as random effect (formula: ~1 | participant). The model's total explanatory power is substantial (conditional R2 = 0.35) and the part related to the fixed effects alone (marginal R2) is of 0.03. The model's intercept, corresponding to ConditionSP = social and MAIA_Total = 0, is at 7.67 (95% CI [-0.21, 15.55], t(1018) = 1.91, p = 0.057). Within this model:
## 
##   - The effect of ConditionSP [polygraph] is statistically significant and negative (beta = -3.69, 95% CI [-7.26, -0.11], t(1018) = -2.02, p < .05; Std. beta = -0.35, 95% CI [-0.45, -0.25])
##   - The effect of MAIA_Total is statistically non-significant and positive (beta = 0.34, 95% CI [-2.59, 3.28], t(1018) = 0.23, p = 0.818; Std. beta = 0.03, 95% CI [-0.20, 0.25])
##   - The interaction effect of MAIA_Total on ConditionSP [polygraph] is statistically non-significant and positive (beta = 0.54, 95% CI [-0.79, 1.87], t(1018) = 0.80, p = 0.426; Std. beta = 0.04, 95% CI [-0.06, 0.14])
##   - The effect of ConditionSP [polygraph] is statistically significant and negative (beta = -3.69, 95% CI [-7.26, -0.11], t(1018) = -2.02, p < .05; Std. beta = 0.91, )
## 
## Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using the Wald approximation.

Hypothesis 2C: Predicted Values of Heartrate Acceleration by Primary SRP3 Subfactor

#Interoception Hypothesis 2C: ECG_Rate_Max -> SRP3_PRI
model_ecg6 <- lme4::lmer(ECG_Rate_Max ~ ConditionSP * SRP3_PRI + (1|participant), data = df2)
parameters::parameters(model_ecg6)
Parameter Coefficient SE CI CI_low CI_high t df_error p Effects Group
(Intercept) 15.0477509 3.4131857 0.95 8.3580299 21.7374720 4.408711 1018 0.0000104 fixed
ConditionSPpolygraph -6.1764211 1.5915639 0.95 -9.2958289 -3.0570132 -3.880725 1018 0.0001041 fixed
SRP3_PRI -0.0805672 0.0415477 0.95 -0.1619991 0.0008647 -1.939152 1018 0.0524828 fixed
ConditionSPpolygraph:SRP3_PRI 0.0488934 0.0194421 0.95 0.0107876 0.0869992 2.514821 1018 0.0119093 fixed
SD (Intercept) 3.5208834 NA 0.95 NA NA NA NA NA random participant
SD (Observations) 2.2933294 NA 0.95 NA NA NA NA NA random Residual 
plot_model(model_ecg6, type = "pred", terms = c("SRP3_PRI", "ConditionSP"), axis.title = c("SRP3 (PRI) Inventory", "Heartrate Acceleration"), title = "Predicted Values of Heartrate Acceleration by SRP3 (PRI) ", legend.title = "Condition") + theme_sjplot(base_size = 10, base_family = "Times New Roman")

report(model_ecg6)
## We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict ECG_Rate_Max with ConditionSP and SRP3_PRI (formula: ECG_Rate_Max ~ ConditionSP * SRP3_PRI). The model included participant as random effect (formula: ~1 | participant). The model's total explanatory power is substantial (conditional R2 = 0.35) and the part related to the fixed effects alone (marginal R2) is of 0.06. The model's intercept, corresponding to ConditionSP = social and SRP3_PRI = 0, is at 15.05 (95% CI [8.36, 21.74], t(1018) = 4.41, p < .001). Within this model:
## 
##   - The effect of ConditionSP [polygraph] is statistically significant and negative (beta = -6.18, 95% CI [-9.30, -3.06], t(1018) = -3.88, p < .001; Std. beta = -0.35, 95% CI [-0.45, -0.25])
##   - The effect of SRP3_PRI is statistically non-significant and negative (beta = -0.08, 95% CI [-0.16, 8.65e-04], t(1018) = -1.94, p = 0.052; Std. beta = -0.21, 95% CI [-0.43, 2.28e-03])
##   - The interaction effect of SRP3_PRI on ConditionSP [polygraph] is statistically significant and positive (beta = 0.05, 95% CI [0.01, 0.09], t(1018) = 2.51, p < .05; Std. beta = 0.13, 95% CI [0.03, 0.23])
##   - The effect of ConditionSP [polygraph] is statistically significant and negative (beta = -6.18, 95% CI [-9.30, -3.06], t(1018) = -3.88, p < .001; Std. beta = 0.91, )
## 
## Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using the Wald approximation.

Correalation Analysis

Hypothesis 1B: Correlation Between ToM and Psychopathy

df5 <- data %>% 
  mutate(yoni_new = yoni_affective + yoni_cognitive) %>% 
group_by(participant) %>% 
  summarise_all(mean)

#Hypothesis 1b
##Yoni Task (1b)
model_H1 <- lm(yoni_affective ~  SRP3_Total, data = df5)
parameters::parameters(model_H1)
Parameter Coefficient SE CI CI_low CI_high t df_error p
(Intercept) 42.2131672 4.2344744 0.95 33.5392396 50.8870948 9.9689273 28 0.0000000
SRP3_Total -0.0026549 0.0291224 0.95 -0.0623096 0.0569997 -0.0911648 28 0.9280107 
plot_model(model_H1, type = "pred", terms = c("SRP3_Total"), axis.title = c("SRP3 Inventory", "Yoni Task (Affective)"), title = "Relationship Between Yoni Task (Affective) and SRP3") + theme_sjplot(base_size = 10, base_family = "Times New Roman")

report(model_H1)
## We fitted a linear model (estimated using OLS) to predict yoni_affective with SRP3_Total (formula: yoni_affective ~ SRP3_Total). The model explains a statistically not significant and very weak proportion of variance (R2 = 2.97e-04, F(1, 28) = 8.31e-03, p = 0.928, adj. R2 = -0.04). The model's intercept, corresponding to SRP3_Total = 0, is at 42.21 (95% CI [33.54, 50.89], t(28) = 9.97, p < .001). Within this model:
## 
##   - The effect of SRP3_Total is statistically non-significant and negative (beta = -2.65e-03, 95% CI [-0.06, 0.06], t(28) = -0.09, p = 0.928; Std. beta = -0.02, 95% CI [-0.40, 0.37])
## 
## Standardized parameters were obtained by fitting the model on a standardized version of the dataset.
correlation::correlation(select(df5,
         yoni_affective,
         SRP3_Total), method = "pearson")
Parameter1 Parameter2 r CI CI_low CI_high t df_error p Method n_Obs
yoni_affective SRP3_Total -0.017226 0.95 -0.3751669 0.3451855 -0.0911648 28 0.9280107 Pearson correlation 30 
##Basic Empathy Scale (1b)
model_H1a <- lm(BES_Affective ~  SRP3_Total, data = df5)
parameters::parameters(model_H1a)
Parameter Coefficient SE CI CI_low CI_high t df_error p
(Intercept) 59.0685800 6.2207642 0.95 46.3259222 71.8112378 9.495390 28 0.000000
SRP3_Total -0.1470311 0.0427831 0.95 -0.2346683 -0.0593939 -3.436664 28 0.001858 
plot_model(model_H1a, type = "pred", terms = ("SRP3_Total"),  axis.title = c("SRP3 Inventory", "BES (Affective)"), title = "Relationship Between BES (Affective) Score and SRP3") + theme_sjplot(base_size = 10, base_family = "Times New Roman")

report(model_H1a)
## We fitted a linear model (estimated using OLS) to predict BES_Affective with SRP3_Total (formula: BES_Affective ~ SRP3_Total). The model explains a statistically significant and substantial proportion of variance (R2 = 0.30, F(1, 28) = 11.81, p = 0.002, adj. R2 = 0.27). The model's intercept, corresponding to SRP3_Total = 0, is at 59.07 (95% CI [46.33, 71.81], t(28) = 9.50, p < .001). Within this model:
## 
##   - The effect of SRP3_Total is statistically significant and negative (beta = -0.15, 95% CI [-0.23, -0.06], t(28) = -3.44, p < .01; Std. beta = -0.54, 95% CI [-0.87, -0.22])
## 
## Standardized parameters were obtained by fitting the model on a standardized version of the dataset.
correlation::correlation(select(df5,
         BES_Affective,
         SRP3_Total), method = "pearson")
Parameter1 Parameter2 r CI CI_low CI_high t df_error p Method n_Obs
BES_Affective SRP3_Total -0.5446749 0.95 -0.756497 -0.2294259 -3.436664 28 0.001858 Pearson correlation 30

Hypothesis 2B: Correlation Between Interoception and Primary Psychopathy

#Hypothesis 2b
##Heartbeat Counting Task(2b)
model_H2 <- lm(HCT_Accuracy ~  SRP3_PRI, data = df5)
parameters::parameters(model_H2)
Parameter Coefficient SE CI CI_low CI_high t df_error p
(Intercept) 0.8561677 0.0550071 0.95 0.7434907 0.9688446 15.564671 28 0.0000000
SRP3_PRI 0.0008599 0.0006722 0.95 -0.0005169 0.0022368 1.279378 28 0.2112645 
plot_model(model_H2, type = "pred", terms = ("SRP3_PRI"), axis.title = c("SRP3 (PRI) Inventory", "HCT Accuracy"), title = "Relationship Between HCT Accuracy and SRP3 (PRI)") + theme_sjplot(base_size = 10, base_family = "Times New Roman")

report(model_H2)
## We fitted a linear model (estimated using OLS) to predict HCT_Accuracy with SRP3_PRI (formula: HCT_Accuracy ~ SRP3_PRI). The model explains a statistically not significant and weak proportion of variance (R2 = 0.06, F(1, 28) = 1.64, p = 0.211, adj. R2 = 0.02). The model's intercept, corresponding to SRP3_PRI = 0, is at 0.86 (95% CI [0.74, 0.97], t(28) = 15.56, p < .001). Within this model:
## 
##   - The effect of SRP3_PRI is statistically non-significant and positive (beta = 8.60e-04, 95% CI [-5.17e-04, 2.24e-03], t(28) = 1.28, p = 0.211; Std. beta = 0.24, 95% CI [-0.14, 0.61])
## 
## Standardized parameters were obtained by fitting the model on a standardized version of the dataset.
correlation::correlation(select(df5,
         HCT_Accuracy,
         SRP3_PRI), method = "pearson")
Parameter1 Parameter2 r CI CI_low CI_high t df_error p Method n_Obs
HCT_Accuracy SRP3_PRI 0.2350084 0.95 -0.1368472 0.5488117 1.279378 28 0.2112645 Pearson correlation 30 
##MAIA2 (2b)
model_H2a <- lm(MAIA_Total ~  SRP3_PRI, data = df5)
parameters::parameters(model_H2)
Parameter Coefficient SE CI CI_low CI_high t df_error p
(Intercept) 0.8561677 0.0550071 0.95 0.7434907 0.9688446 15.564671 28 0.0000000
SRP3_PRI 0.0008599 0.0006722 0.95 -0.0005169 0.0022368 1.279378 28 0.2112645 
plot_model(model_H2, type = "pred", terms = ("SRP3_PRI"), axis.title = c("SRP3 (PRI) Inventory", "MAIA2"), title = "Relationship Between MAIA2 and SRP3 (PRI)") + theme_sjplot(base_size = 10, base_family = "Times New Roman")

report(model_H2a)
## We fitted a linear model (estimated using OLS) to predict MAIA_Total with SRP3_PRI (formula: MAIA_Total ~ SRP3_PRI). The model explains a statistically not significant and weak proportion of variance (R2 = 0.03, F(1, 28) = 0.93, p = 0.343, adj. R2 = -2.39e-03). The model's intercept, corresponding to SRP3_PRI = 0, is at 3.18 (95% CI [2.16, 4.19], t(28) = 6.42, p < .001). Within this model:
## 
##   - The effect of SRP3_PRI is statistically non-significant and negative (beta = -5.83e-03, 95% CI [-0.02, 6.55e-03], t(28) = -0.96, p = 0.343; Std. beta = -0.18, 95% CI [-0.56, 0.20])
## 
## Standardized parameters were obtained by fitting the model on a standardized version of the dataset.
correlation::correlation(select(df5,
         MAIA_Total,
         SRP3_PRI), method = "pearson")
Parameter1 Parameter2 r CI CI_low CI_high t df_error p Method n_Obs
MAIA_Total SRP3_PRI -0.1793627 0.95 -0.5068781 0.193404 -0.9647436 28 0.3429322 Pearson correlation 30

Correlation Between Yoni Task and Basic Empathy Scale

#Correlation between Yoni Task and BES Scale
correlation::correlation(select(df5,
         yoni_new,
         BES_Total), method = "pearson")
Parameter1 Parameter2 r CI CI_low CI_high t df_error p Method n_Obs
yoni_new BES_Total 0.0678549 0.95 -0.2997418 0.4179079 0.3598838 28 0.7216344 Pearson correlation 30 
correlation::correlation(select(df5,
         yoni_affective,
         BES_Affective), method = "pearson")
Parameter1 Parameter2 r CI CI_low CI_high t df_error p Method n_Obs
yoni_affective BES_Affective -0.0164488 0.95 -0.3744987 0.34587 -0.0870507 28 0.9312508 Pearson correlation 30